def test_seeding(space, algo):
"""Verify that seeding after init have no effects"""
optimizer = PrimaryAlgo(space, algo)
optimizer.seed_rng(1)
a = optimizer.suggest(1)[0]
assert not numpy.allclose(a, optimizer.suggest(1)[0])
optimizer.seed_rng(1)
assert not numpy.allclose(a, optimizer.suggest(1)[0])
def test_seeding(space):
"""Verify that seeding makes sampling deterministic"""
bayesian_optimizer = PrimaryAlgo(space, 'bayesianoptimizer')
bayesian_optimizer.seed_rng(1)
a = bayesian_optimizer.suggest(1)[0]
assert not numpy.allclose(a, bayesian_optimizer.suggest(1)[0])
bayesian_optimizer.seed_rng(1)
assert numpy.allclose(a, bayesian_optimizer.suggest(1)[0])
def test_seeding(space):
"""Verify that seeding makes sampling deterministic"""
optimizer = PrimaryAlgo(space, 'hyperband')
optimizer.seed_rng(1)
a = optimizer.suggest(1)[0]
assert not numpy.allclose(a, optimizer.suggest(1)[0])
optimizer.seed_rng(1)
assert numpy.allclose(a, optimizer.suggest(1)[0])
def test_seed_rng(space):
"""Test that algo is seeded properly"""
optimizer = PrimaryAlgo(space, 'hyperband')
optimizer.seed_rng(1)
a = optimizer.suggest(1)
# Hyperband will always return the full first rung
assert numpy.allclose(a, optimizer.suggest(1))
optimizer.seed_rng(2)
assert not numpy.allclose(a, optimizer.suggest(1))
def test_seeding(space):
"""Verify that seeding makes sampling deterministic"""
tpe_optimizer = PrimaryAlgo(space, 'tpeoptimizer')
tpe_optimizer.seed_rng(1)
a = tpe_optimizer.suggest(1)[0]
assert not numpy.allclose(a, tpe_optimizer.suggest(1)[0])
tpe_optimizer.seed_rng(1)
assert numpy.allclose(a, tpe_optimizer.suggest(1)[0])
def test_seeding(space):
"""Verify that seeding makes sampling deterministic"""
optimizer = PrimaryAlgo(space, "meshadaptivedirectsearch")
optimizer.seed_rng(1)
a = optimizer.suggest(1)[0]
with pytest.raises(AssertionError):
numpy.testing.assert_equal(a, optimizer.suggest(1)[0])
optimizer.seed_rng(1)
numpy.testing.assert_equal(a, optimizer.suggest(1)[0])
def test_set_state(space):
"""Verify that resetting state makes sampling deterministic"""
bayesian_optimizer = PrimaryAlgo(space, 'bayesianoptimizer')
bayesian_optimizer.seed_rng(1)
state = bayesian_optimizer.state_dict
a = bayesian_optimizer.suggest(1)[0]
assert not numpy.allclose(a, bayesian_optimizer.suggest(1)[0])
bayesian_optimizer.set_state(state)
assert numpy.allclose(a, bayesian_optimizer.suggest(1)[0])
def test_set_state(space):
"""Verify that resetting state makes sampling deterministic"""
optimizer = PrimaryAlgo(space, 'hyperband')
optimizer.seed_rng(1)
state = optimizer.state_dict
a = optimizer.suggest(1)[0]
assert not numpy.allclose(a, optimizer.suggest(1)[0])
optimizer.set_state(state)
assert numpy.allclose(a, optimizer.suggest(1)[0])
def test_seeding_noisy_grid_search(space):
"""Verify that seeding have effect at init"""
optimizer = PrimaryAlgo(space, {'noisygridsearch': {'seed': 1}})
a = optimizer.suggest(1)[0]
assert not numpy.allclose(a, optimizer.suggest(1)[0])
optimizer = PrimaryAlgo(space, {'noisygridsearch': {'seed': 1}})
assert numpy.allclose(a, optimizer.suggest(1)[0])
optimizer = PrimaryAlgo(space, {'noisygridsearch': {'seed': 2}})
assert not numpy.allclose(a, optimizer.suggest(1)[0])
def test_set_state(space):
"""Test that state is reset properly"""
optimizer = PrimaryAlgo(space, 'hyperband')
optimizer.seed_rng(1)
state = optimizer.state_dict
points = optimizer.suggest(1)
# Hyperband will always return the full first rung
assert numpy.allclose(points, optimizer.suggest(1))
optimizer.seed_rng(2)
assert not numpy.allclose(points, optimizer.suggest(1))
optimizer.set_state(state)
assert numpy.allclose(points, optimizer.suggest(1))
class BayesianOptimizer:
def __init__(self, space, max_trials, seed, **kwargs):
self.primary = PrimaryAlgo(space, {'BayesianOptimizer': kwargs})
self.primary.algorithm.random_state = seed
self.max_trials = max_trials
self.trial_count = 0
@property
def space(self):
return self.primary.space
def is_completed(self):
return self.trial_count >= self.max_trials
def get_params(self, seed=None):
if seed is None:
seed = random.randint(0, 100000)
self.primary.algorithm._init_optimizer()
optimizer = self.primary.algorithm.optimizer
optimizer.rng.seed(seed)
# Giving the same seed could be problematic since optimizer.rng and
# optimizer.base_estimator.rng would be synchronized and sample the same values.
optimizer.base_estimator_.random_state = optimizer.rng.randint(
0, 100000)
params = unflatten(
dict(zip(self.space.keys(),
self.primary.suggest()[0])))
logger.debug('Sampling:\n{}'.format(pprint.pformat(params)))
return params
def observe(self, params, objective):
params = flatten(params)
params = [[params[param_name] for param_name in self.space.keys()]]
results = [dict(objective=objective)]
self.primary.observe(params, results)
def test_deltas_noisy_grid_search(monkeypatch, space):
"""Verify that deltas are applied properly"""
deltas = {'yolo1': 3, 'yolo2': 1}
class Dummy():
def __init__(self, seed):
pass
def uniform(self, a, b, size):
return numpy.ones(size)
monkeypatch.setattr('numpy.random.RandomState', Dummy)
config = {'seed': 3, 'deltas': deltas, 'n_points': 2}
optimizer = PrimaryAlgo(space, {'noisygridsearch': config})
a = optimizer.suggest(4)
assert a[0][0] == -3 + deltas['yolo1'] / 2
assert a[0][1] == numpy.exp(numpy.log(1) + deltas['yolo2'] / 2)
assert a[1][0] == -3 + deltas['yolo1'] / 2
assert a[1][1] == numpy.exp(numpy.log(10) + deltas['yolo2'] / 2)
assert a[2][0] == 3 + deltas['yolo1'] / 2
assert a[2][1] == numpy.exp(numpy.log(1) + deltas['yolo2'] / 2)
assert a[3][0] == 3 + deltas['yolo1'] / 2
assert a[3][1] == numpy.exp(numpy.log(10) + deltas['yolo2'] / 2)
class TPEOptimizer:
def __init__(self, space, max_trials, seed, **kwargs):
self.primary = PrimaryAlgo(space, {'TPEOptimizer': kwargs})
self.primary.algorithm.random_state = seed
self.max_trials = max_trials
self.trial_count = 0
@property
def space(self):
return self.primary.space
def is_completed(self):
return self.trial_count >= self.max_trials
def get_params(self, seed):
if seed is None:
seed = random.randint(0, 100000)
self.primary.algorithm.study.sampler.rng.seed(seed)
self.primary.algorithm.study.sampler.random_sampler.rng.seed(seed)
params = unflatten(
dict(zip(self.space.keys(),
self.primary.suggest()[0])))
logger.debug('Sampling:\n{}'.format(pprint.pformat(params)))
return params
def observe(self, params, objective):
params = flatten(params)
params = [[params[param_name] for param_name in self.space.keys()]]
results = [dict(objective=objective)]
self.primary.observe(params, results)